Overview

Dataset statistics

Number of variables10
Number of observations910
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory78.2 KiB
Average record size in memory88.0 B

Variable types

Numeric10

Alerts

Brinell Hardness is highly overall correlated with Density (g/cc) and 7 other fieldsHigh correlation
Density (g/cc) is highly overall correlated with Brinell Hardness and 7 other fieldsHigh correlation
Electrical Resistivity (ohm-cm) is highly overall correlated with Brinell Hardness and 7 other fieldsHigh correlation
Elongation (%) is highly overall correlated with Density (g/cc) and 2 other fieldsHigh correlation
Melting Point (°C) is highly overall correlated with Brinell Hardness and 7 other fieldsHigh correlation
Modulus of Elasticity (GPa) is highly overall correlated with Brinell Hardness and 8 other fieldsHigh correlation
Specific Heat Capacity (J/g-°C) is highly overall correlated with Brinell Hardness and 7 other fieldsHigh correlation
Thermal Conductivity (W/m-K) is highly overall correlated with Brinell Hardness and 6 other fieldsHigh correlation
Ultimate Tensile Strength (MPa) is highly overall correlated with Brinell Hardness and 7 other fieldsHigh correlation
Yield Tensile Strength(MPa) is highly overall correlated with Brinell Hardness and 4 other fieldsHigh correlation

Reproduction

Analysis started2024-05-28 20:32:00.982540
Analysis finished2024-05-28 20:32:34.184260
Duration33.2 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Yield Tensile Strength(MPa)
Real number (ℝ)

HIGH CORRELATION 

Distinct353
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean361.42813
Minimum7
Maximum1900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2024-05-28T20:32:34.361323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile79.45
Q1150
median241
Q3450
95-th percentile1111
Maximum1900
Range1893
Interquartile range (IQR)300

Descriptive statistics

Standard deviation317.75842
Coefficient of variation (CV)0.87917456
Kurtosis2.3170419
Mean361.42813
Median Absolute Deviation (MAD)117
Skewness1.6554247
Sum328899.6
Variance100970.41
MonotonicityNot monotonic
2024-05-28T20:32:34.666905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165 21
 
2.3%
205 19
 
2.1%
275 18
 
2.0%
240 15
 
1.6%
152 13
 
1.4%
145 13
 
1.4%
193 12
 
1.3%
310 12
 
1.3%
150 12
 
1.3%
110 12
 
1.3%
Other values (343) 763
83.8%
ValueCountFrequency (%)
7 1
0.1%
10 2
0.2%
11 1
0.1%
13.7 1
0.1%
20 1
0.1%
21 1
0.1%
23 1
0.1%
27.25 1
0.1%
27.6 1
0.1%
28 1
0.1%
ValueCountFrequency (%)
1900 1
0.1%
1650 2
0.2%
1520 1
0.1%
1450 2
0.2%
1410 1
0.1%
1365 1
0.1%
1360 1
0.1%
1340 1
0.1%
1330 1
0.1%
1310 2
0.2%

Ultimate Tensile Strength (MPa)
Real number (ℝ)

HIGH CORRELATION 

Distinct337
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean518.82734
Minimum17.5
Maximum2190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2024-05-28T20:32:34.982840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum17.5
5-th percentile133.925
Q1241
median362
Q3668.75
95-th percentile1297.75
Maximum2190
Range2172.5
Interquartile range (IQR)427.75

Descriptive statistics

Standard deviation385.97107
Coefficient of variation (CV)0.74392971
Kurtosis1.2498274
Mean518.82734
Median Absolute Deviation (MAD)157.5
Skewness1.3211027
Sum472132.88
Variance148973.67
MonotonicityNot monotonic
2024-05-28T20:32:35.263082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310 21
 
2.3%
275 19
 
2.1%
655 16
 
1.8%
255 15
 
1.6%
241 15
 
1.6%
290 15
 
1.6%
515 14
 
1.5%
620 13
 
1.4%
585 12
 
1.3%
200 11
 
1.2%
Other values (327) 759
83.4%
ValueCountFrequency (%)
17.5 1
 
0.1%
23 1
 
0.1%
40 1
 
0.1%
45 1
 
0.1%
52 2
 
0.2%
60 1
 
0.1%
75 5
0.5%
76 1
 
0.1%
89.6 1
 
0.1%
90 3
0.3%
ValueCountFrequency (%)
2190 1
0.1%
2100 1
0.1%
2030 1
0.1%
1940 1
0.1%
1830 2
0.2%
1800 1
0.1%
1790 1
0.1%
1690 1
0.1%
1680 1
0.1%
1640 1
0.1%

Modulus of Elasticity (GPa)
Real number (ℝ)

HIGH CORRELATION 

Distinct126
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.6678
Minimum14
Maximum469
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2024-05-28T20:32:35.569725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile45
Q169.6
median78.3
Q3192
95-th percentile225
Maximum469
Range455
Interquartile range (IQR)122.4

Descriptive statistics

Standard deviation63.795152
Coefficient of variation (CV)0.55153769
Kurtosis0.45870872
Mean115.6678
Median Absolute Deviation (MAD)26.7
Skewness0.94320218
Sum105257.7
Variance4069.8214
MonotonicityNot monotonic
2024-05-28T20:32:35.865882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.9 76
 
8.4%
200 75
 
8.2%
71 67
 
7.4%
45 60
 
6.6%
69 49
 
5.4%
70.3 48
 
5.3%
193 25
 
2.7%
190 23
 
2.5%
103 22
 
2.4%
218 21
 
2.3%
Other values (116) 444
48.8%
ValueCountFrequency (%)
14 1
 
0.1%
23.4 1
 
0.1%
32 3
0.3%
40 1
 
0.1%
43 1
 
0.1%
44 7
0.8%
44.1 4
0.4%
44.2 4
0.4%
44.4 2
 
0.2%
44.8 4
0.4%
ValueCountFrequency (%)
469 1
 
0.1%
400 2
 
0.2%
248 1
 
0.1%
232 17
1.9%
229 13
1.4%
225 16
1.8%
218 21
2.3%
216 6
 
0.7%
212 2
 
0.2%
211 1
 
0.1%

Elongation (%)
Real number (ℝ)

HIGH CORRELATION 

Distinct159
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.731055
Minimum0.5
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2024-05-28T20:32:36.155201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile2
Q17
median14
Q325
95-th percentile50
Maximum70
Range69.5
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.4835
Coefficient of variation (CV)0.8168437
Kurtosis0.61390325
Mean17.731055
Median Absolute Deviation (MAD)8.35
Skewness1.1281334
Sum16135.26
Variance209.77179
MonotonicityNot monotonic
2024-05-28T20:32:36.480528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 51
 
5.6%
15 38
 
4.2%
3 35
 
3.8%
12 34
 
3.7%
10 33
 
3.6%
25 29
 
3.2%
13 28
 
3.1%
5 28
 
3.1%
2 25
 
2.7%
4 25
 
2.7%
Other values (149) 584
64.2%
ValueCountFrequency (%)
0.5 6
 
0.7%
1 24
2.6%
1.3 2
 
0.2%
1.5 11
1.2%
1.7 1
 
0.1%
1.8 1
 
0.1%
2 25
2.7%
2.2 1
 
0.1%
2.3 1
 
0.1%
2.4 1
 
0.1%
ValueCountFrequency (%)
70 1
 
0.1%
62.6 1
 
0.1%
61.7 1
 
0.1%
60 5
0.5%
59.3 1
 
0.1%
58.5 1
 
0.1%
58 1
 
0.1%
57 4
0.4%
55.5 1
 
0.1%
55.4 1
 
0.1%

Melting Point (°C)
Real number (ℝ)

HIGH CORRELATION 

Distinct224
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean937.95264
Minimum183
Maximum3370
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2024-05-28T20:32:36.960122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum183
5-th percentile430
Q1595
median650
Q31380
95-th percentile1630
Maximum3370
Range3187
Interquartile range (IQR)785

Descriptive statistics

Standard deviation428.51066
Coefficient of variation (CV)0.45685746
Kurtosis1.3866946
Mean937.95264
Median Absolute Deviation (MAD)222
Skewness0.88534884
Sum853536.9
Variance183621.38
MonotonicityNot monotonic
2024-05-28T20:32:37.301649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1430 42
 
4.6%
1380 21
 
2.3%
1320 19
 
2.1%
603 18
 
2.0%
1365 17
 
1.9%
583.5 17
 
1.9%
1425 17
 
1.9%
1370 17
 
1.9%
628 16
 
1.8%
635 16
 
1.8%
Other values (214) 710
78.0%
ValueCountFrequency (%)
183 2
 
0.2%
232 1
 
0.1%
308.5 1
 
0.1%
316 1
 
0.1%
328 1
 
0.1%
360 5
0.5%
389.5 2
 
0.2%
404.5 3
0.3%
410 5
0.5%
416 1
 
0.1%
ValueCountFrequency (%)
3370 2
 
0.2%
3180 1
 
0.1%
1860 1
 
0.1%
1850 1
 
0.1%
1740 5
0.5%
1710 1
 
0.1%
1700 3
0.3%
1670 6
0.7%
1662 1
 
0.1%
1660 5
0.5%

Density (g/cc)
Real number (ℝ)

HIGH CORRELATION 

Distinct136
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2847912
Minimum1.54
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2024-05-28T20:32:37.831034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.54
5-th percentile1.83
Q12.7
median4.4
Q38.03
95-th percentile9.05
Maximum21
Range19.46
Interquartile range (IQR)5.33

Descriptive statistics

Standard deviation2.9944615
Coefficient of variation (CV)0.5666187
Kurtosis0.82561969
Mean5.2847912
Median Absolute Deviation (MAD)2.57
Skewness0.68960332
Sum4809.16
Variance8.9667999
MonotonicityNot monotonic
2024-05-28T20:32:38.370670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.8 62
 
6.8%
2.7 56
 
6.2%
8 45
 
4.9%
2.69 34
 
3.7%
2.72 34
 
3.7%
2.81 31
 
3.4%
2.71 30
 
3.3%
2.66 28
 
3.1%
1.83 27
 
3.0%
8.3 26
 
2.9%
Other values (126) 537
59.0%
ValueCountFrequency (%)
1.54 1
 
0.1%
1.74 4
 
0.4%
1.77 11
1.2%
1.78 2
 
0.2%
1.8 14
1.5%
1.81 9
 
1.0%
1.82 2
 
0.2%
1.83 27
3.0%
1.84 6
 
0.7%
1.85 3
 
0.3%
ValueCountFrequency (%)
21 1
0.1%
19.3 2
0.2%
19.1 2
0.2%
11.3 1
0.1%
11 1
0.1%
9.69 1
0.1%
9.4 1
0.1%
9.3 1
0.1%
9.29 1
0.1%
9.25 2
0.2%

Electrical Resistivity (ohm-cm)
Real number (ℝ)

HIGH CORRELATION 

Distinct192
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6676857 × 10-5
Minimum1.87 × 10-6
Maximum0.000198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2024-05-28T20:32:38.801258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.87 × 10-6
5-th percentile3.245 × 10-6
Q14.9 × 10-6
median1.05 × 10-5
Q37.2 × 10-5
95-th percentile0.000136
Maximum0.000198
Range0.00019613
Interquartile range (IQR)6.71 × 10-5

Descriptive statistics

Standard deviation4.5875652 × 10-5
Coefficient of variation (CV)1.2508065
Kurtosis1.1234783
Mean3.6676857 × 10-5
Median Absolute Deviation (MAD)6.68 × 10-6
Skewness1.4118782
Sum0.03337594
Variance2.1045754 × 10-9
MonotonicityNot monotonic
2024-05-28T20:32:39.314280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.2 × 10-534
 
3.7%
9.96 × 10-523
 
2.5%
4.6 × 10-621
 
2.3%
0.000136 21
 
2.3%
4.99 × 10-618
 
2.0%
6.4 × 10-617
 
1.9%
0.000101 17
 
1.9%
8.86 × 10-516
 
1.8%
4.4 × 10-616
 
1.8%
4.3 × 10-615
 
1.6%
Other values (182) 712
78.2%
ValueCountFrequency (%)
1.87 × 10-61
 
0.1%
2.7 × 10-61
 
0.1%
2.8 × 10-69
1.0%
2.81 × 10-61
 
0.1%
2.83 × 10-67
0.8%
2.87 × 10-61
 
0.1%
2.9 × 10-66
0.7%
2.99 × 10-64
0.4%
3 × 10-67
0.8%
3.16 × 10-68
0.9%
ValueCountFrequency (%)
0.000198 1
 
0.1%
0.000197 1
 
0.1%
0.00019 8
0.9%
0.000178 5
0.5%
0.00017 1
 
0.1%
0.00016 7
0.8%
0.000157 3
 
0.3%
0.000156 1
 
0.1%
0.000154 2
 
0.2%
0.000144 1
 
0.1%

Specific Heat Capacity (J/g-°C)
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.67889121
Minimum0.116
Maximum1.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2024-05-28T20:32:39.893316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.116
5-th percentile0.375
Q10.43
median0.651
Q30.9
95-th percentile1
Maximum1.05
Range0.934
Interquartile range (IQR)0.47

Descriptive statistics

Standard deviation0.25743693
Coefficient of variation (CV)0.37920203
Kurtosis-1.7126393
Mean0.67889121
Median Absolute Deviation (MAD)0.248
Skewness-0.03647316
Sum617.791
Variance0.066273771
MonotonicityNot monotonic
2024-05-28T20:32:40.308875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.963 109
 
12.0%
0.9 82
 
9.0%
0.46 60
 
6.6%
0.5 59
 
6.5%
0.377 53
 
5.8%
0.88 45
 
4.9%
1 33
 
3.6%
1.05 31
 
3.4%
0.89 25
 
2.7%
0.376 23
 
2.5%
Other values (77) 390
42.9%
ValueCountFrequency (%)
0.116 2
0.2%
0.129 1
 
0.1%
0.134 2
0.2%
0.138 1
 
0.1%
0.173 3
0.3%
0.23 1
 
0.1%
0.285 1
 
0.1%
0.293 1
 
0.1%
0.347 1
 
0.1%
0.356 1
 
0.1%
ValueCountFrequency (%)
1.05 31
 
3.4%
1.04 2
 
0.2%
1.02 6
 
0.7%
1 33
 
3.6%
0.993 1
 
0.1%
0.966 2
 
0.2%
0.963 109
12.0%
0.96 10
 
1.1%
0.95 1
 
0.1%
0.926 1
 
0.1%

Thermal Conductivity (W/m-K)
Real number (ℝ)

HIGH CORRELATION 

Distinct216
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.428736
Minimum1.01
Maximum346
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2024-05-28T20:32:40.643363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.01
5-th percentile6.009
Q115.6
median72.5
Q3138
95-th percentile200
Maximum346
Range344.99
Interquartile range (IQR)122.4

Descriptive statistics

Standard deviation70.277123
Coefficient of variation (CV)0.84236111
Kurtosis-0.93006302
Mean83.428736
Median Absolute Deviation (MAD)60.5
Skewness0.45566893
Sum75920.15
Variance4938.874
MonotonicityNot monotonic
2024-05-28T20:32:40.952129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138 37
 
4.1%
12.5 25
 
2.7%
16.3 25
 
2.7%
134 23
 
2.5%
24.9 22
 
2.4%
12 21
 
2.3%
125 19
 
2.1%
151 18
 
2.0%
10.4 17
 
1.9%
200 17
 
1.9%
Other values (206) 686
75.4%
ValueCountFrequency (%)
1.01 1
 
0.1%
1.87 1
 
0.1%
2.12 1
 
0.1%
2.31 3
0.3%
2.36 3
0.3%
2.38 1
 
0.1%
2.45 2
0.2%
2.95 3
0.3%
3.21 3
0.3%
3.91 1
 
0.1%
ValueCountFrequency (%)
346 1
 
0.1%
315 1
 
0.1%
260 1
 
0.1%
243 1
 
0.1%
240 1
 
0.1%
238 1
 
0.1%
234 7
0.8%
231 2
 
0.2%
230 5
0.5%
227 4
0.4%

Brinell Hardness
Real number (ℝ)

HIGH CORRELATION 

Distinct250
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.43835
Minimum3.9
Maximum594
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2024-05-28T20:32:41.265844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.9
5-th percentile38
Q168
median105
Q3195
95-th percentile360
Maximum594
Range590.1
Interquartile range (IQR)127

Descriptive statistics

Standard deviation104.19764
Coefficient of variation (CV)0.72139869
Kurtosis0.6699015
Mean144.43835
Median Absolute Deviation (MAD)50
Skewness1.180072
Sum131438.9
Variance10857.148
MonotonicityNot monotonic
2024-05-28T20:32:41.562073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75 28
 
3.1%
60 26
 
2.9%
80 26
 
2.9%
85 24
 
2.6%
55 23
 
2.5%
70 21
 
2.3%
120 19
 
2.1%
65 19
 
2.1%
257 16
 
1.8%
105 15
 
1.6%
Other values (240) 693
76.2%
ValueCountFrequency (%)
3.9 1
 
0.1%
5 1
 
0.1%
8 1
 
0.1%
12 1
 
0.1%
14 2
0.2%
17 2
0.2%
19 1
 
0.1%
21 2
0.2%
23 3
0.3%
25 2
0.2%
ValueCountFrequency (%)
594 1
0.1%
485 1
0.1%
476 2
0.2%
463 1
0.1%
460 1
0.1%
459 2
0.2%
451 1
0.1%
444 1
0.1%
434 1
0.1%
427 2
0.2%

Interactions

2024-05-28T20:32:30.467744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:01.291576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:04.810439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:08.051244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:12.194487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:15.290491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:17.980979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:20.724190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:24.241168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:27.655510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:30.747355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:01.573094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:05.063329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:08.497986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:12.618449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:15.571162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:18.257682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:21.025031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:24.648166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:27.949157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:31.007680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:01.819244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:05.322175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:08.825693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:12.923051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:15.839895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:18.516243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:21.282513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:25.004172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:28.236518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:31.268953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:02.091157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:05.567958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:09.245656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:13.165072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:16.091039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:18.791049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:21.545709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:25.360986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:28.493630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:31.528205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:02.357103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:05.833376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:09.719008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:13.635434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:16.359824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:19.067026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:22.084505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:25.748730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:28.779468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:31.808849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:02.623765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:06.130350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:10.203228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:13.905214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:16.642765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:19.319033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:22.367726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:26.158965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:29.065012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:32.078625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:02.891200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:06.526223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:10.633938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:14.198132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:16.919516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:19.593602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:22.663654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:26.545877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:29.372410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:32.357863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:03.202182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:06.951823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:11.024953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:14.480777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:17.198655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:19.900155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:23.041432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:26.844811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:29.663862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:32.608430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:03.448745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:07.290869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:11.364882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:14.742360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:17.447706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:20.163892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:23.382809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:27.110827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:29.931530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:32.865150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:04.521878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:07.669323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:11.752646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:15.024109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:17.721736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:20.460168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:23.829070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:27.368978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-28T20:32:30.204226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-05-28T20:32:41.819156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Brinell HardnessDensity (g/cc)Electrical Resistivity (ohm-cm)Elongation (%)Melting Point (°C)Modulus of Elasticity (GPa)Specific Heat Capacity (J/g-°C)Thermal Conductivity (W/m-K)Ultimate Tensile Strength (MPa)Yield Tensile Strength(MPa)
Brinell Hardness1.0000.5780.7500.2210.6270.807-0.508-0.6180.9480.919
Density (g/cc)0.5781.0000.6420.5290.6230.832-0.899-0.7080.6030.435
Electrical Resistivity (ohm-cm)0.7500.6421.0000.4090.6590.764-0.577-0.9020.7750.617
Elongation (%)0.2210.5290.4091.0000.4950.502-0.585-0.4440.3330.116
Melting Point (°C)0.6270.6230.6590.4951.0000.748-0.625-0.6030.6330.537
Modulus of Elasticity (GPa)0.8070.8320.7640.5020.7481.000-0.744-0.7080.8020.664
Specific Heat Capacity (J/g-°C)-0.508-0.899-0.577-0.585-0.625-0.7441.0000.658-0.564-0.398
Thermal Conductivity (W/m-K)-0.618-0.708-0.902-0.444-0.603-0.7080.6581.000-0.660-0.459
Ultimate Tensile Strength (MPa)0.9480.6030.7750.3330.6330.802-0.564-0.6601.0000.916
Yield Tensile Strength(MPa)0.9190.4350.6170.1160.5370.664-0.398-0.4590.9161.000

Missing values

2024-05-28T20:32:33.536506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-28T20:32:33.967397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Yield Tensile Strength(MPa)Ultimate Tensile Strength (MPa)Modulus of Elasticity (GPa)Elongation (%)Melting Point (°C)Density (g/cc)Electrical Resistivity (ohm-cm)Specific Heat Capacity (J/g-°C)Thermal Conductivity (W/m-K)Brinell Hardness
013.740.023.452.0838.01.540.0000050.632126.0017.0
1225.01830.0211.030.01490.08.800.0000060.44069.20125.0
2362.0413.0248.044.01860.07.190.0000130.46169.10125.0
350.0540.0200.018.01540.07.870.0000090.44076.20146.0
421.090.044.04.0648.01.740.0000051.020159.0030.0
587.0185.044.07.0648.01.740.0000051.020159.0035.0
6130.0200.044.06.0648.01.740.0000051.020159.0046.0
798.0178.044.010.0648.01.740.0000051.020159.0040.0
8241.0496.0159.013.01240.07.440.0001440.4157.79460.0
10290.01070.0469.020.03180.021.000.0000190.13839.60165.0
Yield Tensile Strength(MPa)Ultimate Tensile Strength (MPa)Modulus of Elasticity (GPa)Elongation (%)Melting Point (°C)Density (g/cc)Electrical Resistivity (ohm-cm)Specific Heat Capacity (J/g-°C)Thermal Conductivity (W/m-K)Brinell Hardness
1078205.0515.0193.040.001425.07.920.0000720.50016.3217.0
1079205.0515.0193.040.001425.07.960.0000720.50016.3201.0
1089157.0264.044.13.50592.51.820.0000091.000116.070.0
1090227.5320.544.012.50590.01.840.0000150.96651.380.0
1091127.0193.544.05.50533.01.810.0000141.00084.075.0
1092160.5227.044.16.50575.01.840.0000070.960109.062.5
1093211.0259.044.16.00592.51.850.0000170.96052.085.0
1094103.0150.044.03.00592.51.800.0000061.040100.055.0
1095213.0327.544.18.75590.01.830.0000060.99357.680.0
1096165.0240.045.33.50457.52.020.0001080.95083.078.0